library(tidyverse)
## -- Attaching packages ------------------------------ tidyverse 1.2.1 --
## √ ggplot2 3.2.1 √ purrr 0.3.2
## √ tibble 2.1.1 √ dplyr 0.8.0.1
## √ tidyr 0.8.3 √ stringr 1.4.0
## √ readr 1.3.1 √ forcats 0.4.0
## -- Conflicts --------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(ggplot2)
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
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## last_plot
## The following object is masked from 'package:stats':
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## filter
## The following object is masked from 'package:graphics':
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## layout
library(ggmap)
## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
## Please cite ggmap if you use it! See citation("ggmap") for details.
##
## Attaching package: 'ggmap'
## The following object is masked from 'package:plotly':
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## wind
library(gganimate)
str(amazon)
## 'data.frame': 6454 obs. of 5 variables:
## $ year : int 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 ...
## $ state : Factor w/ 23 levels "Acre","Alagoas",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ month : Factor w/ 12 levels "Abril","Agosto",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ number: num 0 0 0 0 0 10 0 12 4 0 ...
## $ date : Factor w/ 20 levels "1998-01-01","1999-01-01",..: 1 2 3 4 5 6 7 8 9 10 ...
summary(amazon)
## year state month number
## Min. :1998 Rio : 717 Janeiro : 541 Min. : 0.0
## 1st Qu.:2002 Mato Grosso: 478 Abril : 540 1st Qu.: 3.0
## Median :2007 Paraiba : 478 Agosto : 540 Median : 24.0
## Mean :2007 Alagoas : 240 Fevereiro: 540 Mean :108.3
## 3rd Qu.:2012 Acre : 239 Julho : 540 3rd Qu.:113.0
## Max. :2017 Amapa : 239 Junho : 540 Max. :998.0
## (Other) :4063 (Other) :3213
## date
## 1998-01-01: 324
## 1999-01-01: 324
## 2000-01-01: 324
## 2001-01-01: 324
## 2002-01-01: 324
## 2003-01-01: 324
## (Other) :4510
glimpse(amazon)
## Observations: 6,454
## Variables: 5
## $ year <int> 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2...
## $ state <fct> Acre, Acre, Acre, Acre, Acre, Acre, Acre, Acre, Acre, A...
## $ month <fct> Janeiro, Janeiro, Janeiro, Janeiro, Janeiro, Janeiro, J...
## $ number <dbl> 0, 0, 0, 0, 0, 10, 0, 12, 4, 0, 0, 0, 1, 0, 0, 0, 0, 1,...
## $ date <fct> 1998-01-01, 1999-01-01, 2000-01-01, 2001-01-01, 2002-01...
print("Visualizing Data")
## [1] "Visualizing Data"
print("Line graph showing the amount of forest fires per year")
## [1] "Line graph showing the amount of forest fires per year"
df.amazon_1 <- amazon %>% group_by(year)%>%
summarise(numberperyear = round(sum(number)))%>%
ggplot(aes(x = year, y = numberperyear)) + geom_line()
df.amazon_1
print("Scatter Plot showing the occource of forest fires by state and year")
## [1] "Scatter Plot showing the occource of forest fires by state and year"
## Warning: `line.width` does not currently support multiple values.
print("Bar plot showing the total number of forest fires by region")
## [1] "Bar plot showing the total number of forest fires by region"
print("New dataframe with fires sum up by year and state")
## [1] "New dataframe with fires sum up by year and state"
df.amazon <- amazon %>% group_by(year, state) %>% summarise(fires = sum(number))
print("Heatmap of fires by location and year")
## [1] "Heatmap of fires by location and year"
print("An animation showing the amount of fires over the years of each state in brazil")
## [1] "An animation showing the amount of fires over the years of each state in brazil"
print("Creating geodata in order to map fires on to a map of brazil")
## [1] "Creating geodata in order to map fires on to a map of brazil"
states = distinct(amazon, state)
states_df <- as.data.frame(states)
states_df$country <- "Brazil"
states_df$location <- paste(states_df$state, states_df$country, sep=",")
location_df <- mutate_geocode(states_df, location)
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Acre,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Alagoas,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Amapa,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Amazonas,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Bahia,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Ceara,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Distrito+Federal,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Espirito+Santo,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Goias,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Maranhao,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Mato+Grosso,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Minas+Gerais,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Par%C3%A1,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Paraiba,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Pernambuco,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Piau,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## "Piau,Brazil" not uniquely geocoded, using "piau - state of minas gerais, brazil"
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Rio,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Rondonia,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Roraima,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Santa+Catarina,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Sao+Paulo,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Sergipe,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Tocantins,Brazil&key=xxx-dQLFtEZ1fy7un_LrqcwiWOt_WJkN3k
# give state boundaries a white border
l <- list(color = toRGB("white"), width = 2)
# specify some map projection/options
g <- list(
scope = 'south america',
projection = list(type = 'equirectangular'),
showland = TRUE,
landcolor = toRGB("gray85"),
subunitwidth = 1,
countrywidth = 1,
subunitcolor = toRGB("white"),
countrycolor = toRGB("white"))
#adding lon $ lat data back to dataframe
df.amazon$lon <- location_df$lon
df.amazon$lat <- location_df$lat
print("Geo plot of Brazil and it's fires by state")
## [1] "Geo plot of Brazil and it's fires by state"
## Warning: `line.width` does not currently support multiple values.